This study addresses the machining challenges of Hadfield steel by optimizing wire electrical discharge machining (WEDM) parameters through a robust, multimethod approach. The research niche lies in applying hybrid modelling and optimization strategies ‒ specifically combining statistical and soft computing techniques ‒ to enhance machinability of high-manganese steels. The main objective is to improve both material removal rate (MRR) and surface roughness (SR) through systematic parameter tuning. The methodology integrates Taguchi L27 orthogonal array, analysis of variance (ANOVA), and genetic algorithms (GAs) to analyze and optimize five key process parameters: tₒₙ (pulse-on time), tₒff (pulse-off time), gap voltage (Vg), wire feed rate (Wf), and dielectric pressure (Dp). Results revealed that tₒₙ, tₒff, and Vg significantly influenced MRR and SR, while Wf and Dp had negligible effects. The maximum MRR of 36.25 mm²/min (+249.57% from baseline) is achieved under optimal conditions (tₒₙ = 120 µs, tₒff = 30 µs, and Vg = 80 V). The lowest SR of 0.95 µm (46% improvement) is achieved at tₒₙ = 100 µs, tₒff = 40 µs, and Vg = 60 V. Multiobjective optimization using MATLAB’s fmincon solver and GA-based regression modeling yielded a balanced result (MRR = 14.92 mm²/min and SR = 1.76 µm). The SEM imaging and 3D surface topography analysis confirmed that higher SR correlated with craters, microcracks, and cavities due to thermal loading. This work highlights MATLAB’s fmincon optimization combined with genetic algorithm-based modeling as a powerful framework for process optimization, especially for complex geometries such as grooves and splines in Hadfield steel, and underscores both the potential and limitations of WEDM in machining hard-to-cut materials.